Communication, participation, openness to
experience and readiness for organizational change
‘How do communication, participation and openness to experience influence readiness for organizational change?’
- a case at BD Kiestra, the Netherlands -
Master Thesis, MSc BA, Specialization Change Management University of Groningen, Faculty of Economics and Business
27th of August, 2015 THEO BRUINS Student number: 1682873 Rozenstraat 1A 1214 BP Hilversum Tel.: +31 (0)6 25 08 26 68 Email: theobruins@gmail.com Supervisor:
Drs. H.P. (Heleen) van Peet Co-assessor:
Dr. C. (Cees) Reezigt
Acknowledgement
Communication, participation, openness to
experience and readiness for organizational change
How do communication, participation and openness to experience
influence readiness for organizational change?
ABSTRACT
This thesis studied how communication, participation and openness to experience influence readiness for organizational change. Readiness for change was studied on a general construct level, as well on the three dimensions of readiness; cognitive, intentional and emotional readiness for change.Data was gathered through distributing surveys in a technological company located in Drachten in the Netherlands, which resulted in 62 respondents.Regression analysis is used to test the hypotheses. Results indicate that readiness for change is positive and significantly influenced by communication, participation and an employees’ openness to experience. The degree of readiness for change is significantly influenced by openness to experience, though openness to experience cannot be influenced. Of all process factors, participation influenced readiness for change significantly more than communication. Based on the results it can be concluded that companies should focus in particular on participation of employees during organizational change. Furthermore, the limitations of this study, the implications of the results and possible future research will be discussed in this thesis.
Keywords: readiness for change; communication; participation; openness to experience;
TABLE OF CONTENTS
ABSTRACT ... 2
TABLE OF CONTENTS ... 3
1. INTRODUCTION ... 5
1.1-READINESS FOR CHANGE ... 5
1.2-RESEARCH CONTEXT AND CHANGE PROCESS ... 7
1.3-READING GUIDE ... 9
2. THEORY ... 10
2.1-INTRODUCTION ... 10
2.2-READINESS FOR ORGANIZATIONAL CHANGE ... 10
2.3-COMMUNICATION ... 12
2.4-PARTICIPATION ... 13
2.5-PERSONALITY TRAITS ... 15
2.6-CONCEPTUAL MODEL AND OVERVIEW ... 19
3. METHODOLOGY ... 20
3.1-DATA COLLECTION ... 20
3.2-POPULATION AND SAMPLE ... 22
3.3-MEASUREMENTS ... 23
3.4-DATA ANALYSIS ... 26
4. RESULTS ... 33
4.1-DESCRIPTIVE AND CORRELATION ANALYSIS ... 33
4.2-REGRESSION ANALYSIS ... 35
4.3-SUMMARY ... 41
5. DISCUSSION AND CONCLUSION ... 44
5.1-INTRODUCTION ... 44
5.2-FINDINGS ... 44
5.3-IMPLICATIONS ... 48
5.4-LIMITATIONS AND FUTURE RESEARCH ... 49
5.5-CONCLUSION ... 51
APPENDIX A – ONLINE SURVEY ... 58
APPENDIX B – MAIL ... 63
APPENDIX C – ORIGINAL CONSTRUCTS ... 68
APPENDIX D – FACTOR ANALYSIS ... 71
APPENDIX E – NORMALITY DISTRIBUTION ... 79
APPENDIX F – MULTICOLLINEARITY ... 82
APPENDIX G – REGRESSION ... 83
APPENDIX H – STATISTICS OF THE ITEMS ... 91
APPENDIX I – CORRELATION TABLE ... 92
“He not busy being born is busy dying.”
- Bob Dylan
1. INTRODUCTION 1.1 - Readiness for change
Today’s literature shows organizational change gives opportunities to stimulate innovation, to gain or to increase competitive advantage and to keep up with the trends and customer tastes (Vakola & Nikolaou, 2005). The literature also shows all organizations today operate in environments that are characterized by change (Choi & Ruona, 2011). As a lot of change initiatives fail, Szabla (2007) and Burnes (2009) concluded that the change failure rate is higher than 70 percent. Even in a more recent review of the literature on organizational change, Decker et al. (2012) confirmed this percentage and further claimed that the failure rate may be as high as 93 percent. These failures cost organizations billions of dollars each year and have been blamed (in part) for employees’ resistance to change (Van Egeren, 2009). Change is a disruption of routine and even when the change could be beneficial for the employee, they often resist it (Oreg, 2003).
As a change process involves uncertainty and risk for the near future, change recipients may resist change unless there are convincing reasons to do so (Bouckenooghe, 2009). In contrast to this resistance to change is an organization’s readiness for change, a more positive approach to organizational change (Oreg, Vakola, & Armenakis, 2011; Bouckenooghe, 2012). According to Armenakis et al. (1993), readiness for change can be defined as, “organizational members’ beliefs, attitudes and intentions regarding the extent to which changes are needed and the organizations’ capacity to successfully make those changes” (p.681). The literature has shown that successful change initiatives depend on an employees’ committed readiness for change and their support for organizational change initiatives (Armenakis et al., 1993). The focus of this research is on the topic of readiness for organizational change.
agents can persuade the recipients to act on the change and thereby try to increase readiness for the change. Communication has been identified as one of the key predictors of successful organizational change (Lewin, 1951; Kotter, 1996). Devos et al. (2007) found that employee participation is beneficial to have during a change and Ford, Ford and D’Amelio (2008) state that communication and participation is often used at the same time during a change process. Bouckenooghe et al. (2009) studied the importance of the quality of communication and the participation of change recipients in a change. Participation in decision making related to change, in actual change projects, or in training is positively related to openness or commitment to change and negatively related to cynicism (resistance) to organizational change (Choi, 2011).
In 2002, Armenakis & Harris (2002) found that communicating a clear and consistent message and employees’ participation in a change can create a positive attitude towards organizational change. Lewis (2000) states that communication about the change and participation within the change seems to be interrelated with the creation of a vision for the future and the support for and help to improve the change as it develops. Additionally, the failing change initiative has traditionally been studied from a management perspective and failure of change initiatives have been credited to management or change agents’ negligence of the change processes (Bouckenooghe, et al., 2009; Rafferty et al., 2013).
Recent researches, however, have shown the role of personality traits in recipients’ readiness for change, since personality traits have been linked to behavioral responses (Saksvik & Hetland, 2009; Vakola, 2004; Omazic et al., 2011; Van Egeren, 2009). Five personality traits have been identified in the literature, referred to as the ”Big Five”: (1) Extraversion; (2) Agreeableness; (3) Conscientiousness; (4) Emotional Stability and (5) Intellect or Openness to experience (Goldberg, 1993). The Big Five can forecast work attitude and behavior of an individual, while dimensions such as cognition, intention and affection/emotion have been shown to inform employees’ behavior in regards to readiness for change (Bouckenooghe et al., 2009).
interact to produce behavioral results, and readiness for change expresses itself in behavioral outcomes (Van Egeren, 2009). However, a recent study by Omazic et al. (2011) analyzed the effect of personality traits on organizational readiness for change and produced results that conflict with other evidence in the literature on personality traits and the response of readiness to organizational change. These contradicting results and hence the dissonance and gap in literature provides an opportunity to investigate and extend the literature on factors that influence readiness for change. This research shows the comparison between the traditional process factors communication and participation and the influence of personality traits.
Based on the above, the central research question is as follows:
To what extent do communication, participation and personality traits influence readiness for organizational change?
1.2 - Research context and Change process
This study was performed at BD Kiestra (part of Becton Dickinson), a mid-size organization (approximately 300 employees), located in the northern part of the Netherlands and operating in the healthcare industry. BD Kiestra produces Total Lab Automation solutions for clinical microbiology. These systems are modular, scalable, and are designed to increase efficiency and streamline processes of laboratories. All systems are automated and partly customizable.
handful of systems every year. Now BD Kiestra produces standardized subassemblies, and they produce the modules in series. These modules are used to produce a WCA (work cell automation) or a TLA system (total lab automation) and are now built in less than ten weeks. This year they expect to produce around 30 systems and continue to grow in the upcoming years.
FIGURE 1
Growth forecast BD Kiestra - internal presentation (2015)
* FY = Fiscal Year
*InoqulA, WCA & TLA are types of machines
This major change in the production process, from a project-oriented organization to a series production organization, has resulted in many new systems and processes on the work floor. Since the transformation of the organization, the following have been implemented:
• ISO 13485 (e.g. procedures, work instructions, quality checks, etc.) • SAP 4.0B (e.g. implementation, training, business intelligence, etc.) • Lean Manufacturing (e.g. Six Sigma, Kaizen, CI projects, etc.)
1.3 - Reading guide
2. THEORY 2.1 - Introduction
This research shows how communication, participation, and personality traits influence readiness for organizational change. In order to further explain this research and its purpose, the different concepts or topics relevant are described in-depth. First, readiness for change is defined, continuing with the concept of communication and subsequently participation. After that, I elaborate on the topic of personality traits influencing readiness for change. At the end of this chapter, a conceptual model is presented.
2.2 - Readiness for Organizational change
In the next paragraph, I examine readiness for organizational change, its definition, history, views and its dimensions.
2.2.1 - Definition of readiness for organizational change
support for a change effort (Armenakis et al., 1993; Holt et al., 2007) or as the degree to which an employee is willing to participate in an organizational activity that is different from routine activities (Desplaces et al. 2005), and mental and physical preparedness for an experience (Walinga, 2008). Bouckenooghe et al. (2009) described readiness for change as a concept that contains cognitive, emotional, and intentional dimensions. Earlier, Holt et al. (2007) argued that readiness was “a comprehensive attitude that is influenced simultaneously by the content, process, context, and individuals involved” (p. 235). Attitude towards change had been earlier conceptualized by Guttman (1976) as “a tri-dimensional concept that encompassed cognitive, affective (emotional), and intentional/ behavioral components” (p. 501).
the decision by the employee to resist or support the change intended (Piderit, 2000; Oreg, 2006). To investigate the role of readiness for change in this research, the instrument of Bouckenooghe et al. (2009) was used.
2.3 - Communication
In the next paragraph, I examine communication and the relation to readiness for organizational change.
2.3.1 - Definition of communication
Bouckenooghe et al. (2009) referred to communication in the change context as the way of translating the change message. The components of effective communication are clarity of information, frequency of messaging, and openness about the change content. Daft (1997) defines communication as the process by which information is exchanged and understood by two or more people and is mostly used to motivate or influence behavior. Burnes (2009) underlines this crucial function of communication for all change activities. Armenakis and Harris (2009) define persuasive communication in a change situation as, “transmitting the message components to change recipients” (p. 135).
Multiple researchers identify communication as the first step in creating recipients readiness for organizational change (Armenakis & Harris, 2002; Kotter, 1995; Rafferty & Restubog, 2010). To have an effective implementation of change, communication is vital (Bordia et al., 2004). Random communication predominantly results in negative rumors and ambiguity of the change, which has a negative influence on readiness (Bouckenooghe, 2012). According to Reichers et al. (1997), management needs to minimize the rumors and needs to inform the change recipients properly.
Bouckenooghe et al. (2009) found that the quality of change communication is the most cited process factor of readiness for change. Bouckenooghe et al. (2009) and Miller et al. (1994) define quality of change communication as, “How change is communicated. The clarity, frequency, and openness determine whether communication is effective” (p. 599). Clarity of the communication is more than simply hearing from (top) management about the change; it is also about communicating the reasons for the change and the employees’ understanding of them. Frequency is the amount of communication about the specific change, and openness of the communication refers to “how much” details/information is given about the change, as the motives and the opportunities in shaping and implementing the organizational change (Bouckenooghe, 2012). Furthermore, communication helps employees become aware of the upcoming change, it makes them more salient and it helps them to reframe the intended change (Weick, 1999). Bouckenooghe et al. (2009) provides validated questions about the quality of communication. These are based on Oreg’s recent meta-analysis (2006) and the study of Miller et al. (1994). Since these validated questions capture the concept of quality of communication in just a small set of questions (these questions can be found in Appendix C), these were used in this study.
The research shows that good communication is key for a successful and/or positive organizational change. Communication is about translating the change message. Important components of the quality of communication are clarity of information, frequency or amount of communication and openness about the change content.
The above conclusions result in the following hypothesis:
H1: The higher the quality of communication about organizational change, the higher the level of readiness for change of employees.
2.4 - Participation
2.4.1 - Definition of participation
Considerable research has been completed in the field of employee participation, specifically during a change. According to Glew et al. (1995), employee participation is one of the “oldest” research fields within the change area, and this has resulted in multiple definitions of recipients’ participation. The pioneering study of Coch and French (1948) found that groups who participate in the implementation and elaboration of a change had less resistance than groups who did not. Similarly, Lewin (1951) concluded that participation is useful in changing attitudes during the change process.
Burnes (2009) defines participation in his book as, “participation is the process of involving people in decision-making and change activities within organizations” (p. 600). Similarly, Bouckenooghe et al. (2009) states in their article that, “participation is the extent to which staff members are involved in and informed about decisions that directly concern them, decisions about organizational change inclusive” (p. 598). So are procedures and guidelines discussed bottom up and is the workforce (frontline) should be involved in the change process (Bouckenooghe et al., 2009). Whelan & Somerville (2010) state, “participation involves employees in tasks, specifically related to the change initiative” (p.182). Based on these definitions, participation involves the employee in the change process, which makes sure that the employee takes part in the change (Armenakis et al., 2007; Cunningham et al., 2002).
change process is a crucial condition to create readiness for organizational change. Oreg et al. (2011) concluded that participation increased change recipient involvement, which increased the overall readiness for organizational change. Holt et al. (2007) discovered in their research that employees who actively participated during a change were more likely to be positive towards readiness for change than non-participants.
Therefore, it can be concluded that participation of employees is important for readiness for organizational change. Participation increases the positive attitude of an employee towards organizational change. Employees who participate more are more ready for change.
The above conclusions result in the following hypothesis:
H2: The more a change process is characterized by participation, the higher the level of readiness for change of employees
2.5 - Personality traits
The following paragraphs elaborate on personality traits in relation to readiness for change. This section also describes the conflict in literature about the influence of personality traits on readiness for change and about the concept of “openness to experience,” one of the personality traits.
2.5.1 - Definition of personality traits
between personality traits and behavior more thoroughly, the chance of better outcomes would increase (Goldberg, 1993).
2.5.2 - Personality traits and readiness for change
As mentioned above, personality is characterized by personality traits. Not only are those traits common worldwide and affect our behavior, they are also influenced by environmental circumstances like experiences gained in the course of time (Goldberg, 1993; Vakola, 2004; Van Egeren, 2009). Two leading models facilitate analysis of personality traits. The first model analyzes 16 different personality traits; the second model analyzes five different personality traits. This model is also known as the “Big Five” (Goldberg, 1993).
Since 1940, personality traits have been studied. As stated in Block (2005), Allport created a large list of different personality traits, but this was not very useful to identify a personality because of its size. Cattell reduced this large amount to 171 personality traits items and later to 16 different personality traits by factor analysis (Block, 1995). In 1950, Cattell developed a questionnaire that measured the 16 personality traits. Since that time, numerous researchers continued to study the different traits, and in 1980, Goldberg succeeded in developing five broad factors of personality, based on the 16 personality traits of Cattell (see Table 1). Today these factors are known as the Big Five factors of personality, also called as the Five Factor Model (Goldberg, 1993). Since the identification of the Big Five, many studies have used this model in researching the relationship between personality and behavior. One of the findings is that an analysis using the Big Five model can predict a persona’s behavior in an organization, and it also provides insight in interpersonal and group synergy (Mount, Barrick & Stewart, 1998).
among different change events” (p. 538). According to these researchers, these change schemes are related to an individual’s reaction to change. The reason for this may be that these schemes are influenced by personality traits.
TABLE 1
Big Five of Goldberg (1993)
Factor # Personality trait Facet description Polar opposite
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Extraversion / Surgency Agreeableness Conscientiousness Emotional Stability
Intellect / Openness to Experience
Sociable Affable Well-organized Insecure Creative/resourceful Introverted Reserved Wasteful Self-assured Wary/guarded
Within the literature, a consensus exists regarding the role of personality traits in influencing a readiness for change (Costa and McCrae, 1992; Goldberg, 1990). Vakola (2004) found that personality traits (as she called them, individual characteristics) are linked to organizational change. Vakola (2004) states, based on findings of Judge et al. (1999), that: “the profile of the ‘positive to change’ employee is, an extrovert, open to new experiences, agreeable and a conscientious employee” (p.103). Change agents selected based on this profile can contribute significantly to the overall success of organizational change efforts, and thus readiness (Saksvik & Hetland, 2009; Omazic et al., 2011; Van Egeren, 2009). Moreover, Judge et al. (1999) found specifically that openness to experience, one of the personality traits, correlated positively with the change recipients’ readiness for organizational change.
As most researchers discovered, a correlation exists between the role of personality traits and employees’ readiness for change. However, Omazic et al. (2011) found in their recent study, a conflict with this correlation between personality traits and readiness for change. In their research, they found no correlation between personality traits and readiness for change (Omazic et al., 2011).
for change. Their studies indicate that personality traits interact to produce behavioral results, and readiness for change expresses itself in behavioral outcomes as readiness for change (Van Egeren, 2009). These different outcomes constitute a gap in literature.
2.5.3 - Openness to experience
Due to time limitations and my interest in the influence of personality traits (i.e. personality characteristics) of the Big Five on readiness for organizational change, I focus on one of the five personality traits, openness to experience. Research has related openness to experience to effective coping and adjustment to change. More specifically, McCrae & Costa (1987) found a relation between openness to experience and how people successfully manage to deal with stress. Therefore, openness to experience can be linked to a positive approach of a recipient against change, as people who can successfully deal with stress situations, are more positive and open-minded towards change. As stated previously in this chapter, Vakola (2004) stated that the “positive to change” employee requires several (personality) traits, including openness to new experiences.
Multiple researchers have concluded that personality traits influence the employees’ readiness for organizational change. However, some researchers disagree with this conclusion and state that there is no or a lack of evidence for this finding (Omazic et al., 2011). Openness to experience is one of the five personality traits, and seems to have – based on most literature – a positive influence on readiness for change.
The above conclusions results in the following hypothesis:
2.6 - Conceptual model and overview
In Figure 2, a conceptualization is shown of the antecedents influencing readiness to organizational change, which is investigated in this study. In Table 2, an overview of the research question and the hypotheses is presented. Based on the previous chapter, the research question is reformulated to be more specific.
FIGURE 2
Conceptual model of this study
TABLE 2
Overview research question, reformulated RQ and hypotheses
Old RQ
To what extent do communication, participation and personality traits influence readiness for organizational change?
Reformulated RQ
To what extent do communication, participation and openness to experience influence readiness for organizational change?
H1 The higher the quality of communication about organizational change, the higher the
level of readiness for change of employees.
H2 The more a change process is characterized by participation, the higher the level of
readiness for change of employees.
H3 The more an employee exhibits openness to experience, the higher the level of readiness
for change of employees.
3. METHODOLOGY
This chapter describes the research methodology used for this research. It begins with the methodology of data collection; then I elaborate on the measurements, and finally I elaborate on how the analysis is conducted. This research is quantitative survey-based. Quantitative research is the best method to investigate relationships to find causal effects between the dependent and independent variables and is widely used to test hypotheses (Van de Ven & Poole, 2005).
3.1 - Data collection
The data collection was conducted at the production department of BD Kiestra, located in The Netherlands. In this department, there are 88 people working in ten teams, each with a team manager. This department was involved in a large change trajectory. This change trajectory is described in the chapter 1.2 of this study.
Questionnaire
In this study, a self-administered Internet survey was carried out via the online program Qualtrics Survey Software. An introduction was given at the beginning of the survey, introducing the research and researcher, explaining the purpose of the study, and ensuring anonymity and confidentiality.
Advantages and disadvantages of Internet surveys
Process of the Internet survey
As stated previously, in this research a self-administered Internet survey was conducted. As there was a limited response time, a reminder was sent six days after the first email, again containing the link to the survey. Both messages ended up in the spam filter of the employees’ email, so after ten days and only a response rate of 25 percent, the invitation link was sent directly by the manager (my contact). As the deadline was fast approaching, the production manager and team managers informed their employees about this survey. The deadline was extended until June 19, and on June 19, the survey received some attention during a “canteen-meeting” with all the employees of the production department. Finally, the survey was terminated on the June 19 with a 70 percent response rate (more details in paragraph 3.2). The emails sent can be found in Appendix B.
Content
The survey consisted of 33 questions related to the concepts of communication about the change, participation, openness to experience, and readiness for change. All questions were based on existing scales. As there was a validated Dutch translation of Bouckenooghe’ questions, namely Veranderklimaat in Organisaties (Bouckenooghe et al., 2009), these were used. The questions about openness to experience were translated and checked by the university supervisor. The original constructs and their translation can be found in Appendix C.
Scale
Pre-survey semi structured interview and test
After creating the first version of the survey, since validated and tested questions are being used, there was an interview with the coordinator of the production department of BD Kiestra. This interview was conducted to give insights to the researcher about the situation within BD Kiestra, the roles of the team managers and employees, and the specific change process. This pre-survey interview was also used to discuss the questions and to make sure the survey was clear and understandable. Based on this interview, minor linguistic changes were made. According to Thomas (2004), testing the survey is an essential step in the design process. This survey was tested with two employees to see if all the questions were clear to the employees. The complete survey can be found in Appendix A.
3.2 - Population and Sample
For this research, 62 employees completed the survey correctly; 88 surveys were distributed. This represents a response rate of 70.45%. This response is high compared to the average response rate of this kind of research which is 47.1%. This percentage is based on 357 equivalent studies (Anseel et al., 2010). This high response was achieved by sending reminder emails, a personal internal mail of a manager, and involvement of the production manager.
TABLE 3
Overview gender, age and tenure
N Gender Age (mean) Tenure (mean)
Overall 62 100 % 36.76 years 38.84 months (3.24 years)
Male 54 87.1 % 37.52 years 40.5 months (3.38 years)
Female 8 12.9 % 31.63 years 27.63 months (2.30 years)
Specific characteristics per sub-department
Sub dep-artment
N N tot
Response Gender Age mean (years) Tenure mean (months) Tenure Min:Max (months) DEP1 9 12 75.0 % Male: 100% (n=9) Female: 0% (n=0) 35.1 28.2 22 : 39 DEP2 6 10 60.0 % Male: 100% (n=6) Female: 0% (n=0) 44.0 80.3 19 : 100 DEP3 11 13 84.6 % Male: 90.9% (n=10) Female: 9.1% (n=1) 34.7 31.9 19 : 72 DEP4 6 12 50.0 % Male: 66.7% (n=4) Female: 33.3% (n=2) 37.6 24.0 6 : 36 DEP5 4 8 50.0 % Male: 75% (n=3) Female: 25% (n=1) 30.25 28.8 15 : 58 DEP6 6 8 75.0 % Male: 100% (n=6) Female: 0% (n=0) 31.0 43 14 : 72 DEP7 2 4 50.0 % Male: 100% (n=2) Female: 0% (n=0) 35 125 60 : 190 DEP8 3 3 100 % Male: 100% (n=3) Female: 0% (n=0) 43 74.7 6 : 192 DEP9 3 3 100 % Male: 66.7% (n=2) Female: 33.3% (n=1) 42 23.3 7 : 43 DEP10 12 15 80.0 % Male: 75% (n=9) Female: 25% (n=3) 38.25 42.8 14 : 120 Total 62 88 70.45% Male: n=54 Female: n=8 36.76 38.84 3.3 - Measurements
TABLE 4
Measurements per variable
Variables Measure
Readiness for change (dependent) OCQ (Bouckenooghe et al., 2009) Communication (independent) OCQ (Bouckenooghe et al., 2009) Participation (independent) OCQ (Bouckenooghe et al., 2009) Openness to experience (independent) Big Five, IPIP 6FPQ (Goldberg, 2006)
Dependent variable Readiness for change
Independent variables Communication and Participation
For measuring the concepts of communication and participation, the scales of the OCQ_C, P, R from Bouckenooghe et al. (2009) were used. Bouckenooghe et al. (2009) made an effort to develop scales on the concepts of communication and participation by measuring the quality of change communication (COM) and participation (PAR). Bouckenooghe et al. (2009) states that the items in these scales measure the degree to which communication about the change is understandable and how management tries to generate support for the change by participation. The communication scale consisted of eight items (from which six items were used) and the participation scale consisted of eleven items (from which eight items were used). These eight items were used because they were most relevant to the situation of the change occurring at BD Kiestra, the maximum amount of questions was restricted, and two communication items were more focused on leadership which is not within this research scope. Examples of the items include, “I am regularly informed on how the change is going” (COM) and “Departments are consulted about the change sufficiently” (PAR).
Independent variable Openness to experience
items include, “I prefer variety to routine” (positively coded) and “I am not interested in abstract ideas” (reverse coded).
3.4 - Data analysis
For the data analysis, the program IBM SPSS Statistics for Mac - version 22 - was used for analyzing the data in order to test the hypotheses. The next section provides an overview of the analysis.
Exploring data
After finishing the data collection process, the data were checked for missing and strange values and mistakes. All respondents completed the survey, so no datasets were removed because they did not complete the whole survey. After this first check, the second step was to recode negative items to positive. In total eight items (specific COGRE 1-3 and OPTE 6-10, COM6 and PAR7) were re-coded in advance. In order to perform proper analyses, all items need to be stated in the same way. In the end, 62 datasets were appropriate for the analyses.
Factor analysis
Even though all the items in the questionnaire came from validated scales or studies, a factor analysis was conducted to determine if the items in the questionnaire were actually questioning the constructs and which were not. Field (2005) states in his research that factor analysis is used to recognize “clusters or groups” of variables. Some measures or conditions should be met when conducting a factor analysis:
- Eigenvalue (EV) > 1;
- Kaiser-Meyer-Olkin (KMO) = All items should have a factor load of > 0.5. KMO values between 0.7 and 0.8 are considered “good,” values between 0.8 and 0.9 are “great,” and values > 0.9 are “superb” (Hutcheson & Sofroniou, 1999);
- The Bartlett’s Test of Sphericity should have a statistical significance, so < 0.05.
that retain as much of the original measures’ variance as possible (Conway & Huffcut, 2003; Field, 2009). Furthermore, oblique rotation (Oblimin) was used. This rotation was preferred above the orthogonal rotation (or Varimax rotation). Conway & Huffcutt (2003) state in their research, “If factors really are correlated (a likely situation), then orthogonal rotation forces an unrealistic solution that will probably distort loadings away from simple structure, whereas an oblique rotation will better represent reality and produce better simple structure” (p. 153). Since it was hypothesized that the factors are correlated, an oblique rotation was chosen.
Reliability analysis
Cronbach’s Alpha was used as a reliability analysis to check the internal consistencies. Cronbach’s Alpha scores of < 0.6 are “poor,” between 0.6 and 0.7 are “acceptable,” and scores of > 0.8 are considered as “good.” Below, the results of the factor analyses are shown.
Factor analysis: Readiness for Change (dependent variable)
The dependent variable readiness for change consisted of nine items. These items of Bouckenooghe et al. (2009) were distributed over three dimensions, namely three items for cognitive readiness (COGRE1-3), three items for intentional readiness (INRE1-3) and three items for emotional readiness (EMRE1-(INRE1-3). The first PCA was conducted with oblique rotation (Oblimin) and EigenValue (EV) > 1. The overall Kaiser-Meyer-Olkin (KMO) value = 0.799 (good) and the KMO value of individual items were > 0.66, which is above the acceptable limit of 0.5. Bartlett’s test of Sphericity is χÇ (36) = 450.985, p < 0.000, which means that correlations between items were sufficiently large.
removed from the scale and a second PCA was run, now with EV as fixed number of factors on three (because of the three dimensions).
Following the removal of the factor for COGRE1, the items loaded on the correct factor as intended. The overall KMO value = 0.790 (good) and the KMO value of individual items were > 0.85, which is significantly above the acceptable limit of 0.5. Bartlett’s test of Sphericity is χÇ (28) = 437.810, p < 0.000, which means that correlations between items were sufficiently large.
Table 5 shows the factor loadings of the final PCA, as well as the items to continue with. Furthermore, the factor reliability scores are the following:
• Cronbach’s α COGRE = 0.761 (acceptable) • Cronbach’s α INRE = 0.951 (good)
• Cronbach’s α EMRE = 0.913 (good)
TABLE 5
Factor loadings: Dependent variable Rescaled Component 1 2 3 COGRE2 .861 COGRE3 .916 INRE1 .971 INRE2 1.010 INRE3 .851 EMRE1 .903 EMRE2 .984 EMRE3 .851
Factor analysis: COM, PAR, OPTE (independent variables)
from Goldberg (2006). Communication consisted of six items (COM1-6), participation consisted of eight items (PAR1-8), and openness to experience consisted of ten items (OPTE1-10). This PCA was conducted with oblique rotation (Oblimin) and EigenValue (EV) > 1.
After the first PCA rotation, the overall KMO value = 0.749 (good), but the KMO value of individual items is not clear. Some items have negative loadings and some items have a value of 0.40, which is below the acceptable value (should be > 0.5). The Bartlett’s test of Sphericity is χÇ (276) = 984.190, p < 0.000, which means that correlations between items were sufficiently large. Furthermore, the items loaded on five factors, where only a loading on three factors (COM, PAR and OPTE) was expected. More details of all the steps taken can be found in Appendix D.
A second rotation was conducted; the negative loaded factors from the previous rotation (COM6, PAR7 and OPTE6) were re-coded, and as the theory explains there are three factors where the items should be loaded on, so the EV is fixed on three factors. Following the second rotation, the overall KMO value = 0.771 (good), but an unclear loading of the items on the factors remained.
A third rotation was conducted; the re-coded factors (COM6, PAR7 and OPTE6) were removed to determine if this changed the outcome. The overall KMO value = 0.771 (good), but there was still no clear loading on the factors. The items of COM and PAR loaded on the same factor, which was not desirable, but OPTE loaded on two factors. As this was a diffuse outcome of OPTE, the next step was to remove the strongest items of OPTE for a better loading of the other items on their expected factor.
test of Sphericity is χÇ (136) = 766.587, p < 0.000, which means that correlations between items were sufficiently large.
In the next rotation conducted, the item PAR3 was removed because of a double loading, and the items PAR4 and PAR8 were removed because of a loading on a wrong factor. After this rotation, the items loaded on the correct factor as was expected. The overall KMO value = 0.812 (great) and the KMO value of individual items were > 0.56, which is above the acceptable limit of 0.5. Bartlett’s test of Sphericity is χÇ (91) = 601.862, p < 0.000, which means that correlations between items were sufficiently large.
Table 6 shows the factor loadings of the final analysis of the independent variables, as well as the items to continue with. Furthermore, the factor reliability scores are the following:
• Cronbach’s α COM = 0.922 (good) • Cronbach’s α PAR = 0.791 (acceptable) • Cronbach’s α OPTE = 0.902 (good)
TABLE 6
Normality
To determine if the data of this research are normally distributed and thus parametric tests could be used, a couple of tests were completed. First, the Durbin-Watson test (DW) was conducted, as the DW score tests the null hypothesis that the residuals from a regression are not auto-correlated and thus have a normal distribution. As the DW score (1.475) was inconclusive (see Appendix E for more details), another test was conducted to determine if there was a normal distribution.
The dependent and independent variables in a regression model do not need to be normally distributed by themselves; only the prediction errors need to be normally distributed (Field, 2009; Stock and Watson, 2011). A test for normally distributed errors is a normal probability plot of the residuals. If the distribution is normal, the points on such a plot should fall close to the diagonal reference line. As shown in Appendix E Figure 1, the dots hover fairly close to the diagonal line. As plots are never inconclusive because of the visual observation (Field, 2009), it can be concluded that there is a normality distribution in the residuals, although this should be met with caution.
In order to determine if there is homoscedasticity, the plot of standardized residuals against standardized predicted values should look like a random array of dots. As the dots in the plot look random and quite evenly distributed throughout the plot, the assumptions of linearity and homoscedasticity are met (although with caution); these findings can be found in Appendix E, Figure 2, 3, 4 and 5.
Correlation
outliers in the data (Pallant, 2002). As there are some outliers in the data of this research, this is a valid choice. In order to determine the strength of the relationship between two variables, the value of r must be examined. According to Pallant (2002), when r is 0.10 to 0.29 or r is -0.10 to -0.29, the value is small. When r is 0.30 to 0.49 or r is -0.30 to -0.49, the value is medium. When r is 0.50 to 1.0 or r is -0.50 to -1.0, the value is high. A perfect positive correlation is +1 and a perfect negative correlation is -1. The significance level should be p < 0.05.
Regression analysis
4. RESULTS
This chapter elaborates on the results of this research. To begin with, the results of the quantitative data are presented, including a correlation and regression analysis. These analyses were conducted to show how strong the relationship between the variables is. Clustered variables were created after the factor analysis. READgem is readiness for change as a construct and consists of the three dimensions COGRE, INRE and EMRE.
4.1 - Descriptive and Correlation analysis
Table 7 shows the mean and standard deviation for each variable in the analysis. Descriptive details of all the single items can be found in Appendix H. Furthermore, the table provides an overview of all correlations, using Spearman’s Rho, among variables, together with the reliability coefficient of the aggregated variables. The correlation analysis has been done one-tailed, as all hypotheses are formulated with a positive correlation with to readiness for change. Age is shown in years, tenure is shown in months and all the other variables were measured on a 7-point Likert scale. The next part describes the correlation analysis.
Age and tenure
Age is significantly positive correlated with cognitive readiness (COGRE) (r = .326, p = .005), intentional readiness (INRE) (r = .212, p = .049) and emotional readiness (EMRE) (r = .454, p = .000). Age is also significantly positively correlated to readiness for change (READ) (r = .432, p = .000). Age is also significantly positively correlated with communication (COM), r = .450 and participation (PAR), r= .372 (both p < .01). Tenure is significantly negatively correlated to emotional readiness (r = -.269, p = .17). Age and tenure are not correlated with openness to experience (OPTE).
related to age or tenure of an employee. This seems logical as a personality characteristic does not become higher or lower when getting older.
Readiness for change
There is a significantly strong positive correlation between readiness for change (all three dimensions) and communication, r = .623 and participation, r = .623 (both p < .01). Also openness to experience has a significantly positive correlation with readiness for change (r = .279, p = .014). The dimensions cognitive, intentional and emotional readiness all have a significant positive correlation with communication, as well with participation (for all r > .336 and p < .01). Openness to experience has a positive correlation with intentional readiness (r = .503, p = .000).
With respect to readiness for change, all independent variables were positively related and thus important. However, openness to experience is only positively correlated with intentional readiness. This indicates that when an employee is more open to new experiences, he/she is more willing to take actions to support the change.
Independent variables
There is a significant positive correlation between communication and participation (r = .661, p = .000).
Multicollinearity
TABLE 7
Correlation analysis, means and standard deviations for the (aggregated) variables
4.2 - Regression analysis
As introduced at the start of this chapter, this section describes the regression analyses. By performing regression analyses, the proposed hypotheses can be tested. These analyses include two steps. The first step is a simple linear regression analysis, and the second step is a multiple regression analysis. Table 8 and 9 show the results of this analysis. The Beta in these tables are the unstandardized beta coefficients.
4.2.1 - Linear regression
In this section, the hypotheses of this research is discussed based on the linear regression analyses. Tables 8 and 9 show the regression analyses (more details in Appendix G). The beta values of the variables are close together, indicating an equal influence on readiness for change.
N = 62 1 2 3 4 5 6 7 8 9 1 1. COGREgem - 2. INREgem .433* - 3. EMREgem .474* .564* - 4. COMgem .477* .336* .628* - 5. PARgem .359* .480* .650* .661* - 6. OPTEgem .197 .503* .163 .046 .051 - 7. READ .702* .784* .890* .623* .656* .279* - 8. Age (years) .326* .212* .454* .450* .372* -.102 .432* - 9. Tenure (months) -.040 -.083 -.269* .016 -.125 -.043 -1.58 -.078 - Mean 4.61 5.51 4.45 4.04 4.04 6.08 4.89 36.76 38.84 Std Dev 1.24 1.21 1.42 1.46 1.15 .86 1.06 11.10 35.04
TABLE 8
Linear regression analysis - readiness for change (all 3 dimensions combined)
Dependent variable
Readiness for change (all 3 dimensions) B SE Sig R2 F Communication .434* .075 .000 .360 33.793* Participation .571* .093 .000 .386 37.725* Openness .502* .145 .001 .167 12.014* * p < .05 Communication
The first hypothesis stated, “the higher the quality of communication about organizational change, the higher the level of readiness for change of employees.” The variable Communication (COM) measures this hypothesis and shows a positive significant influence on readiness to change. B = .434, R² = .360, p < .05. As a result, hypothesis H1 is accepted.
Participation
The second hypothesis stated, “the more a change process is characterized by participation, the higher the level of readiness for change of employees.” The variable Participation (PAR) measures this hypothesis and shows a positive significant influence on readiness to change. B = .571, R² = .167, p < .05. As a result, hypothesis H2 is accepted.
Openness to Experience
The third hypothesis stated, “the more an employee exhibits openness to experience, the higher the level of readiness for change of employees.” The variable Openness to Experience (OPTE) measures this hypothesis and shows a positive significant influence on readiness to change. B = .502, R² = .186, p < .05. As a result, hypothesis H3 is accepted.
Readiness for change – Three dimensions
TABLE 9
Linear regression analysis – three dimensions of readiness for change
Dependent variable
Cognitive readiness for change
B SE Sig R2 F Communication .385* .097 .000 .206 15.613* Participation .416* .128 .002 .149 10.500* Openness .196 .185 .292 .018 1.129 * p < .05 Dependent variable Intentional readiness for change
B SE Sig R2 F Communication .290* .100 .005 .123 8.421* Participation .474* .121 .000 .203 15.306* Openness .792* .150 .000 .318 28.037* * p < .05 Dependent variable Emotional readiness for change
B SE Sig R2 F Communication .611* .097 .000 .397 39.511* Participation .473* .124 .000 .393 38.812* Openness .415 .206 .051 .063 4.078* * p < .05 Communication
The variable communication (COM) shows a positive significant influence on cognitive readiness to change. B = .385, R² = .206, p < .05. Communication also shows a positive significant influence on intentional readiness to change. B = .290, R² = .123, p < .05. Furthermore communication shows a strong, positive, significant influence on emotional readiness for change. B = .611, R² = .397, p < .05.
Participation
.05. Furthermore communication shows a strong positive significant influence on emotional readiness for change. B = .473, R² = .393, p < .05.
Openness to Experience
The variable openness to experience (OPTE) shows a strong, positive, significant influence on intentional readiness to change. B = .792, R² = .318, p < .05. Openness to experience shows no positive, significant influence on emotional readiness for change as the significance is p = .051, B = .415, R² = .063. Likewise, openness to experience does not show any significance with cognitive readiness to change. B = .196, R² = .018, p < .292.
With respect to cognitive readiness for change, no relationship was found with openness to experience. This indicates that the personality characteristic openness to experience does not influence cognitive readiness, as cognitive readiness is also more about general thoughts of the change, instead of individual relativeness to the change. Another interesting thing to notice is the strong Beta value of openness to experience was found in relation to intentional readiness and at the same time the quite low communication Beta score. Appendix G contains more detailed data from the linear regression analyses.
4.2.2 – Multiple regression
Table 10 shows the results of the multiple regression analysis of readiness for change and the independent variables communication, participation and openness to experience. Table 11 shows the multiple regression analyses of the three dimensions and the independent variables.
analysis and is used to estimate the expected shrinkage in R² (as a result of too many independent variables). The value of the Adjusted R² = .537, and thus the value is close to the value of R² so minimal shrinkage occurred based on this value.The F value tests whether the overall regression model is a good fit for the data. Table 10 shows that the independent variables is a statistically significant predictor of the dependent variable (F(3, 58) = 24.573, p < 0.01). In other words, the regression model is a good fit of the data.
This multiple regression between readiness for change and the independent variables communication, participation, and openness to experience shows the following results: All three variables were a statistically significant predictor for readiness for change, F(3, 58) = 24.573, p < .01, R² = .560. All three variables showed statistical significance to the prediction, p < 0.05, where the highest score is from openness to experience, with B = .406 and the lowest score of Communication, B = .229. Based on this multiple regression analysis, hypotheses H1, H2 and H3 can be reconfirmed.
TABLE 10
Multiple regression analysis - readiness for change
Dependent variable Readiness for change B SE Sig Communication .229* .083 .008 Participation .348* .106 .002 Openness .406* .108 .000 R .748 R2 .560 Adjusted R2 .537 F-value 24.573* *p < .05
Readiness for change – Three dimensions
TABLE 11
Multiple regression analysis - three dimensions of readiness for change
Dependent variable
Cognitive readiness for change
B SE Sig Communication .224* .129 .027 Participation .163 .164 .327 Openness .115 .168 .497 R .476 R2 .227 Adjusted R2 .187 F-value 5.668* *p < .05 Dependent variable Intentional readiness for change
B SE Sig Communication .051 .083 .623 Participation .370* .132 .007 Openness .727* .135 .000 R .688 R2 .473 Adjusted R2 .446 F-value 17.348* *p < .05 Dependent variable Emotional readiness for change
B SE Sig Communication .362* .118 .003 Participation .449* .150 .004 Openness .278 .153 .074 R .712 R2 .506 Adjusted R2 .481 F-value 19.827* *p < .05
Based on the results above, the following findings are interesting:
Communication and participation are the only variables that can be influenced by the company. As the Beta score of communication is quite low (.229) and the Beta score of participation is higher (.348), the company should focus more on influencing the participation of employees to increase the readiness for change.
Another interesting finding is the high Beta score of openness to experience in relation to intentional readiness for change, in both the linear regression (Table 9) (B = .792) as the multiple regression (Table 11) (B = .727). As intentional readiness for change refers to the extent to which an individual is willing to take actions to support the change and openness to experience is considered a personality characteristic, the company should focus on recruiting employees with a high score on openness to experience.
To expand on above, openness to experience is a personality characteristic and cannot be influenced by interventions of the company; therefore, a high openness to experience can only be achieved when the company selects employees on their level of openness to experience (for instance, during an assessment in the pre-selection phase).
Table 13 shows an overview of all the accepted or rejected hypotheses, including the multiple regression results. All the results of the multiple regression analyses can be found in detail in Appendix G.
4.3 - Summary
TABLE 12
Overview of all hypotheses
H1 The higher the quality of communication about organizational change, the higher
the level of readiness for change of employees.
H1a The higher the quality of communication about organizational change, the higher the level
of cognitive readiness for change of employees.
H1b The higher the quality of communication about organizational change, the higher the level
of intentional readiness for change of employees.
H1c The higher the quality of communication about organizational change, the higher the level
of emotional readiness for change of employees.
H2 The more a change process is characterized by participation, the higher the level of
readiness for change of employees.
H2a The more a change process is characterized by participation, the higher the level of
cognitive readiness for change of employees.
H2b The more a change process is characterized by participation, the higher the level of
intentional readiness for change of employees.
H2c The more a change process is characterized by participation, the higher the level of
emotional readiness for change of employees.
H3 The more an employee exhibits openness to experience, the higher the level of
readiness for change of employees.
H3a The more an employee exhibits openness to experience, the higher the level of cognitive
readiness for change of employees.
H3b The more an employee exhibits openness to experience, the higher the level of intentional
readiness for change of employees.
H3c The more an employee exhibits openness to experience, the higher the level of emotional
readiness for change of employees.
TABLE 13
Overview accepted or rejected hypotheses
Hypo-thesis Variable Construct/ dimension Correlation Linear regression Multiple regression Accepted/ rejected
H1 COM READ ✔ ✔ ✔ Accepted
H1a COM COGRE ✔ ✔ ✔ Accepted
H1b COM INRE ✔ ✔ ✘ Rejected
H1c COM EMRE ✔ ✔ ✔ Accepted
H2 PAR READ ✔ ✔ ✔ Accepted
H2a PAR COGRE ✔ ✔ ✘ Rejected
H2b PAR INRE ✔ ✔ ✔ Accepted
H2c PAR EMRE ✔ ✔ ✔ Accepted
H3 OPTE READ ✔ ✔ ✔ Accepted
H3a OPTE COGRE ✘ ✘ ✘ Rejected
H3b OPTE INRE ✔ ✔ ✔ Accepted
H3c OPTE EMRE ✘ ✘ ✘ Rejected
5. DISCUSSION AND CONCLUSION 5.1 - Introduction
In this chapter, the results of the previous chapters are discussed. The goal of this chapter is to generate insights but also to generate a deeper understanding of these topics. In this research, the following research question was central: “To what extent do communication, participation and openness to experience influence readiness for organizational change?” The answers to the hypotheses are given per construct in paragraph 5.2. In paragraph 5.3, theoretical and managerial implications are given. Paragraph 5.4 identifies the limitations of this research and provides suggestions for future research. In paragraph 5.5, the conclusion regarding this research will be given.
5.2 - Findings
The purpose of this study is to examine the potential effects of communication, participation, and openness to experience on the change recipients’ readiness for organizational change. Based on research from Bouckenooghe et al. (2009), the construct readiness for change was divided into the dimensions of cognitive, intentional, and emotional readiness for change, to determine the influence of the independent variables on these dimensions as well as the influence on the whole constructs of readiness for change.
5.2.1 - Communication
relationship between communication and cognitive and emotional readiness, but no significant relation with intentional readiness for change.
These findings suggest that the quality of communication is a predictor for recipients’ readiness for change for most of the factors. This is in line with expectations as Oreg (2006) states that communication could increase the recipients’ readiness for change, but only when used correctly. He states that the relationship between information and readiness for change is related to the content (quality), rather than the amount of information given (and thus communication). The difference of the linear (B = .290 and significant) and multiple regression analysis (B = .051 and not significant) could be attributed to the stability of the personality characteristic openness to experience variable; the personality of an employee does not change with interventions. Intentional readiness refers to the extent to which an employee is willing to take actions to support the change (e.g. contribution, energy and devotion to the change) (Piderit, 2000; Oreg, 2006), but when the personality characteristic openness to experience became stronger (which cannot be influenced), the process factors and especially communication became weaker. In conclusion of this construct, the quality of communication influences positively the recipients’ readiness for change as a construct and on most dimensions.
5.2.2 - Participation
These findings suggest that participation is primarily a predictor for positive recipients’ readiness for change. Nonetheless, Russ (2008) states that a change characterized by more participation is no guarantee for positive attitudes towards readiness for change, as some participation approaches can be seen as dishonest (e.g. asking for input (participation) without actually using this input). This is also mentioned by one of the employees: “they [the company] asked for input, but I never heard or saw any feedback on it.” The organization should carefully use participation to increase recipients’ readiness for change.
A possible explanation for no significant relationship between cognitive readiness and participation could be that participation was in essence not part of the change process initiated the management. The items in the survey about participation were meant to measure the level of participation during the change, but as cognitive readiness is about “the intended change,” the employees were not meant to participate in this change. Another explanation could be the low total respondents that may have influenced the results and why no relation was found. In conclusion of this construct, active participation positively influences the recipients’ readiness for change as a construct and on most dimensions.
5.2.3 - Openness to experience
Many researchers state that personality traits contribute significantly to the overall success of organizational change efforts and, as a result, readiness (Saksvik & Hetland, 2009; Omazic et al., 2011; Van Egeren, 2009). More specifically, McCrae & Costa (1987) indicated a positive relationship between openness to experience and readiness. In contrast, Omazic et al. (2011) found no relationship between personality traits (and thus also openness to experience) and readiness for change.
multiple regression analysis. A significant and positive correlation was found between openness to experience and intentional readiness. Additionally, the linear and multiple regression analysis showed a significant, positive (causal) relationship between openness to experience and intentional readiness.
The multiple regression Beta score of openness to experience is the highest score in relation with readiness for change (Openness B = .406, Communication B = .229, Participation B = .348). A possible explanation for this score could be that openness to experience is a stable personality characteristic, and thus it cannot be influenced (i.e. the management cannot create more openness with interventions as this is a personality trait). Based on this research, openness to experience does influence readiness for change the most, but openness itself cannot be influenced. When comparing the linear regression Beta scores with the multiple regression scores, in the multiple regression analysis, openness became the strongest variable, and the Beta score of communication decreased drastically. Openness became almost two times as big as communication. The severe decrease of the Beta score of communication and the high score of openness indicates that openness influences communication as well (i.e. mediates).
5.3 - Implications
Theoretical implications
Different implications for theory can be drawn as a result of this research:
• The importance of communication is confirmed. When the quality of communication is high, employees receive more details of the change and are more ready for change. When the quality of communication received by employees is poor, this may cause feelings of uncertainty against the change by the change recipient.
• Furthermore, the importance of participation has also been confirmed. When a change is more characterized by participation, the change recipients feel they are more involved in the change process and will show less resistance and more readiness for change. However, management should be prepared to act on the input provided.
• As openness to experience, one of the personality traits of the Big Five, has a positive influence on readiness for change, the influence of other personality traits of the Big Five on readiness for change should be investigated. This additional research will be interesting because the research of Omazic et al. (2011) state that personality traits (in general) have no influence on readiness for change at all.
• Openness to experience became the strongest variable in the multiple regression analysis, and similarly communication decreased immensely, which suggests that openness to experience mediates between communication and readiness for change. This would be an interesting paradox to further investigate.
Managerial implications
Based on this research, several managerial or practical implications can be drawn.
recipients’ readiness for change. This can be achieved by soliciting feedback on ideas of employees at a monthly team meeting, for instance. This will give them a sense of participation in the “process.”
• Based on this study, openness to experience has the most influence on readiness for change. As openness to experience is a stable personality characteristic, it cannot be influenced by interventions. If BD Kiestra wants employees that have a high readiness for change, they should look for employees with a high score on the personality characteristic openness to experience (Big Five). Including a Big Five personality questionnaire in the recruitment process and selecting employees with a high score on openness to experience can achieve this. Openness to experience should be taken into account if this selection prerequisite is needed for the specific function.
• BD Kiestra should complete one change project before starting a new one. Employees commented that they are getting confused about the ongoing changes. This unclear message/information and thus a lack of clear communication (quality) can increase resistance to the change or decrease readiness for the change.
5.4 - Limitations and Future research
Although the purpose of this study was to test any significant relationship as completely and correctly way of possible, every research has its limitations, and this research is no exception to that.
